Natsume Hiroharu, Okamoto Shogo
Department of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan.
Foods. 2024 Nov 23;13(23):3755. doi: 10.3390/foods13233755.
The temporal dominance of sensations (TDS) method has received particular attention in the food science industry due to its ability to capture the time-series evolution of multiple sensations during food tasting. Similarly, the temporal liking method is used to record changes in consumer preferences over time. The conjunctive use of these methods provides an effective framework for analyzing food taste and preference, making them valuable tools for product development, quality control, and consumer research. We employed the TDS and temporal liking data of strawberries that were recorded in our earlier study to estimate the temporal liking values from sensory changes. For this purpose, we used a reservoir network, a type of recurrent neural network suitable for time-series data. The trained models exhibited prediction accuracy of the determination coefficient as high as 0.676-0.993, with the median being 0.951. Further, we proposed two types of sensitivities of each sensory attribute toward the change in the temporal liking value. Elemental sensitivity indicates the degree that each sensory attribute influences the temporal liking. In the case of strawberries, the sweet attribute was the greatest contributor, followed by the attribute of fruity. The two least-contributing attributes were light and green. Interactive sensitivity indicates how each attribute affects the temporal liking in conjunction with other attributes. This sensitivity analysis revealed that the sweet attribute positively influenced the liking, whereas the green and light attributes impacted it negatively. The proposed methods offer a new approach to comprehensively analyze how the results of TDS are linked to those of the temporal liking method, serving as a step toward developing an alternative system to human panels.
由于能够捕捉食品品尝过程中多种感官的时间序列演变，感官时间主导性（TDS）方法在食品科学行业受到了特别关注。同样，时间喜好方法用于记录消费者偏好随时间的变化。这些方法的联合使用为分析食品口味和偏好提供了一个有效的框架，使其成为产品开发、质量控制和消费者研究的宝贵工具。我们利用在早期研究中记录的草莓的TDS和时间喜好数据，从感官变化中估计时间喜好值。为此，我们使用了一种适合时间序列数据的递归神经网络——储层网络。训练后的模型显示，决定系数的预测准确率高达0.676 - 0.993，中位数为0.951。此外，我们提出了每种感官属性对时间喜好值变化的两种敏感度类型。元素敏感度表明每种感官属性对时间喜好的影响程度。以草莓为例，甜味属性的贡献最大，其次是果味属性。贡献最小的两个属性是清淡味和青味。交互敏感度表明每种属性与其他属性共同作用时对时间喜好的影响。这种敏感度分析表明，甜味属性对喜好有积极影响，而青味和清淡味属性对其有负面影响。所提出的方法提供了一种新的途径，用于全面分析TDS结果与时间喜好方法结果之间的联系，是朝着开发替代人工评判小组的系统迈出的一步。
